1 citations · 1 across the 12 of their papers we have counts for
12 papers
Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions
Anastasiia Sedova, Natalie Schluter, Skyler Seto +1
Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. When target language data is s…
Scaling Laws for Mixture Pretraining Under Data Constraints
Anastasiia Sedova, Skyler Seto, Natalie Schluter +1
As language models scale, the amount of data they require grows -- yet many target data sources, such as low-resource languages or specialized domains, are inherently limited in si…
Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
Paul Jeha, Anastasiia Sedova, Louis Béthune +4
For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization…
Optimal Splitting of Language Models from Mixtures to Specialized Domains
Skyler Seto, Pierre Ablin, Anastasiia Filippova +4
Language models achieve impressive performance on a variety of knowledge, language, and reasoning tasks due to the scale and diversity of pretraining data available. The standard t…
Which Data Matter? Embedding-Based Data Selection for Speech Recognition
Zakaria Aldeneh, Skyler Seto, Maureen de Seyssel +8
Modern ASR systems are typically trained on large-scale pseudo-labeled, in-the-wild data spanning multiple domains. While such heterogeneous data benefit generalist models designed…
Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks
Eileen Pan, Anna Seo Gyeong Choi, Maartje ter Hoeve +2
Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English diale…
Assessing the Role of Data Quality in Training Bilingual Language Models
Skyler Seto, Maartje ter Hoeve, Maureen de Seyssel +1
Bilingual and multilingual language models offer a promising path toward scaling NLP systems across diverse languages and users. However, their performance often varies wildly betw…
Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks
Maureen de Seyssel, Jie Chi, Skyler Seto +3
We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). I…
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging
Pierre Ablin, Angelos Katharopoulos, Skyler Seto +1
Machine learning models are routinely trained on a mixture of different data domains. Different domain weights yield very different downstream performances. We propose the Soup-of-…
Training Bilingual LMs with Data Constraints in the Targeted Language
Skyler Seto, Maartje ter Hoeve, Richard He Bai +2
Large language models are trained on massive scrapes of the web, as required by current scaling laws. Most progress is made for English, given its abundance of high-quality pretrai…
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
Chen Huang, Skyler Seto, Samira Abnar +3
Large pretrained vision-language models like CLIP have shown promising generalization capability, but may struggle in specialized domains (e.g., satellite imagery) or fine-grained…
Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling
David Grangier, Simin Fan, Skyler Seto +1
Specialist language models (LMs) focus on a specific task or domain on which they often outperform generalist LMs of the same size. However, the specialist data needed to pretrain…